Assistant Professor of Medicine
Kevin has a rare combination of skills from his prior undergraduate and PhD training, which encompasses both (wet-lab based) molecular genetic/epigenetic and (dry-lab) bioinformatics and biostatistics expertise. Kevin is working on advanced computational analysis using TCGA data.
Maryam is a Postdoc in the Stanford Center for Expanded Data Annotation and Retrieval (CEDAR). She works on developing novel mechanisms to assist biomedical scientists with the intelligent annotation, cataloging, and retrieval of experimental datasets.
Mu Zhou earned Ph.D. degree in Computer Science and Engineering, advised by Dr. Lawrence Hall and Dr. Dmitry Goldgof at the University of South Florida in 2015. His research interests are focused on the intersection of precision medicine and data mining. During his Ph.D. study, he has been involved in cross-disciplinary research projects, directed by Dr. Robert Gatenby and Dr. Robert Gillies at H. Lee. Moffitt Cancer Research Institute, Tampa. In particular, he worked on the field of quantitative cancer imaging for tumor response assessment in various domains (e.g., brain, breast, sarcoma, and lung cancers). His research aims to develop computational models that integrate clinical information (e.g., imaging, genomic, and clinical records) to predict cancer treatment outcomes and improve personalized healthcare.
Magali's research focuses on the integration of multi-omics data for cancer investigation. She is working on two projects that involve AMARETTO, an algorithm developed by the lab that constructs a module network to connect driver genes to clusters of co-expressed genes. The first one is an extension of this algorithm to a pancancer analysis on 11 cancer sites simultaneously and the second one the integration of miRNA data to investigate the effect of miRNAs in these module networks.
Haruka’s research has focused on imaging genomics in brain tumors. She has developed an approach that flips the common analysis framework, by starting from the imaging phenotype of a solid tumor instead of it’s molecular characterization. She has used quantitative characterizations of human brain tumors using their MR images to define subgroups. This led to three brain tumor subgroups that she successfully validated in an external validation data set and was able to match with molecular pathway activities. This result showed thought-provoking implicates with regards to treatment for brain tumors.
Katie is focusing on developing algorithms on meta-analysis of biomedical data. She developed CoINcIDE, a method for meta-clustering across multiple biomedical data sets.
Marcos focuses on the statistical analysis of DNA methylation in cancer. He uses and extends, MethylMix, a method which identifies differentially methylated and transcriptionally predictive genes, in samples from single or combined cancer sites.
Darvin is focusing on developing novel algorithms to process imaging data. He has extensive experience in segmentation algorithms of medical images including brain and lung tumor segmentation.
Alice is working on integrating genomic and epigenomic data to identify cancer driver genes. She is focusing on using existing and developing novel algorithms for multi-omics data fusion
Julie is focusing her medical studies on informatics and data driven medicine. She is intersted in how epigenomics defines subtypes of cancer patients and how this can impact precision medicine.
Elysia is interested in adolescent and young adult cancer patients. She aims at using bioinformatics algorithms to identify commonalities among these cancer patients and what differentiates them from other cancer patients.